43 research outputs found
Improving Neural Question Generation using Answer Separation
Neural question generation (NQG) is the task of generating a question from a
given passage with deep neural networks. Previous NQG models suffer from a
problem that a significant proportion of the generated questions include words
in the question target, resulting in the generation of unintended questions. In
this paper, we propose answer-separated seq2seq, which better utilizes the
information from both the passage and the target answer. By replacing the
target answer in the original passage with a special token, our model learns to
identify which interrogative word should be used. We also propose a new module
termed keyword-net, which helps the model better capture the key information in
the target answer and generate an appropriate question. Experimental results
demonstrate that our answer separation method significantly reduces the number
of improper questions which include answers. Consequently, our model
significantly outperforms previous state-of-the-art NQG models.Comment: The paper is accepted to AAAI 201
On the Generation of Medical Question-Answer Pairs
Question answering (QA) has achieved promising progress recently. However,
answering a question in real-world scenarios like the medical domain is still
challenging, due to the requirement of external knowledge and the insufficient
quantity of high-quality training data. In the light of these challenges, we
study the task of generating medical QA pairs in this paper. With the insight
that each medical question can be considered as a sample from the latent
distribution of questions given answers, we propose an automated medical QA
pair generation framework, consisting of an unsupervised key phrase detector
that explores unstructured material for validity, and a generator that involves
a multi-pass decoder to integrate structural knowledge for diversity. A series
of experiments have been conducted on a real-world dataset collected from the
National Medical Licensing Examination of China. Both automatic evaluation and
human annotation demonstrate the effectiveness of the proposed method. Further
investigation shows that, by incorporating the generated QA pairs for training,
significant improvement in terms of accuracy can be achieved for the
examination QA system.Comment: AAAI 202
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Coupled with the availability of large scale datasets, deep learning
architectures have enabled rapid progress on the Question Answering task.
However, most of those datasets are in English, and the performances of
state-of-the-art multilingual models are significantly lower when evaluated on
non-English data. Due to high data collection costs, it is not realistic to
obtain annotated data for each language one desires to support.
We propose a method to improve the Cross-lingual Question Answering
performance without requiring additional annotated data, leveraging Question
Generation models to produce synthetic samples in a cross-lingual fashion. We
show that the proposed method allows to significantly outperform the baselines
trained on English data only. We report a new state-of-the-art on four
multilingual datasets: MLQA, XQuAD, SQuAD-it and PIAF (fr).Comment: 7 page
Generating Highly Relevant Questions
The neural seq2seq based question generation (QG) is prone to generating
generic and undiversified questions that are poorly relevant to the given
passage and target answer. In this paper, we propose two methods to address the
issue. (1) By a partial copy mechanism, we prioritize words that are
morphologically close to words in the input passage when generating questions;
(2) By a QA-based reranker, from the n-best list of question candidates, we
select questions that are preferred by both the QA and QG model. Experiments
and analyses demonstrate that the proposed two methods substantially improve
the relevance of generated questions to passages and answers.Comment: Accepted by EMNLP 201
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Proceedings of QG2010: The Third Workshop on Question Generation
These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge".
QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)